Over the past decade numerous data management systems have enjoyed increasing popularity and use throughout the world. Some examples of data management systems currently available include, but are not limited to, business and personal tax preparation systems, financial management systems, personal and business accounting systems, personal and business record keeping and inventory systems, healthcare expense management systems, receipt management systems, and numerous other applications/systems that help a user organize, process, import, and use, various types of data from various sources of data.
One important function provided by, or associated with, many data management systems is the ability to obtain/convert source data from source documents, and/or source document data, into data that can be imported to, and/or processed by, the data management systems. This importation process typically involves using Optical Character Recognition (OCR), or similar systems, to identify relevant source data from various source documents and convert the source data into import data that is then used to auto-fill appropriate data entry fields in one of more digital forms used with the data management system.
As a specific illustrative example, when the data management system is tax preparation system, a tax preparer, or other tax professional, who desires to prepare one or more tax related forms for a client typically obtains one or more source documents, or image data representing the one or more source documents, from the client, e.g., W-2 forms, 1099-B forms, interest or dividend documents, mortgage payment documents, etc.
In this specific illustrative example, the one or more source documents are then processed using an OCR system, or similar functionality, to identify and extract various import data, such as income, interest paid amounts, dividend amounts, mortgage interest paid, etc., from the one or more source documents. In this specific example, the import data is data that will be needed to prepare and process one or more tax forms for the client. In this specific illustrative example, the import data is processed by the tax preparation system and/or used by the tax preparation system to auto-fill various fields of one or more tax forms for the client.
While the source data identification and importation process discussed above provides data management system users a powerful and time-saving tool, scanning, OCR, data entry field mapping, and other procedures associated with the data importation process are still imperfect. As a result, data management system users are often required to monitor and review the data importation process and, in many cases, the data management system users must make various corrections to the imported data and/or data entry fields auto-filled with the import data. This review/editing/correction process is often viewed quite unfavorably by data management system users and, as in many instances in life, the data management system users tend to focus on the errors generated rather than on the amount of data that was correctly imported.
While it is often human nature to focus on mistakes, particularly when the observer is the party who must correct the mistakes, this focus is particularly problematic for providers of data management systems. This is because the most frequent complaint regarding many data management systems, and the often cited reason for discontinuing the use of data management systems, is the perception that the data importation process is inaccurate and produces an unacceptable number of errors. In some cases, data management system users are even left with the impression that the time required to correct data importation errors completely offsets the time saved by using the data management system.
Despite the perception of many data management system users that an unacceptable number of errors are introduced by the importation process, typically only a very small percentage of errors are actually introduced by the data importation process. However, currently, users of data management systems are not provided any information indicating the actual percentage of errors introduced, or any indication of how well the data importation process really does work, other than having to focus on correcting the relatively few errors that are introduced. Clearly this current situation leaves the data management system users with a skewed impression of the efficiency of the data management systems and the value added.
What is needed is a method and system that provides a specific user of a data management system, and/or a provider of the data management system, an empirically-based indication of the accuracy of a given data importation event and how well the data management system is actually working for the specific user of the data management system.